U.S. patent number 10,268,690 [Application Number 15/474,313] was granted by the patent office on 2019-04-23 for identifying correlated content associated with an individual.
This patent grant is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to James E. Bostick, John M. Ganci, Jr., Martin G. Keen, Sarbajit K. Rakshit.
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United States Patent |
10,268,690 |
Bostick , et al. |
April 23, 2019 |
Identifying correlated content associated with an individual
Abstract
A computer-implemented method includes: receiving, by a
computing device, a plurality of content objects from one or more
computer content source devices; extracting, by the computing
device, metadata from the plurality of content objects; storing, by
the computing device, a plurality of records having the extracted
metadata in a repository, wherein each record identifies a time in
which a statement was made by an individual regarding a topic;
identifying, by the computing device, correlated content between
the plurality of content objects based on comparing the metadata in
the records, wherein the correlated content includes a plurality of
statements made by the individual regarding the topic at different
periods of time; generating, by the computing device, a correlated
content object having the correlated content; and providing, by the
computing device, the correlated content object to a user
device.
Inventors: |
Bostick; James E. (Cedar Park,
TX), Ganci, Jr.; John M. (Cary, NC), Keen; Martin G.
(Cary, NC), Rakshit; Sarbajit K. (Kolkata, IN) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION (Armonk, NY)
|
Family
ID: |
63670558 |
Appl.
No.: |
15/474,313 |
Filed: |
March 30, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180285359 A1 |
Oct 4, 2018 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
16/40 (20190101); G06F 40/30 (20200101); G06F
16/48 (20190101); G06F 40/289 (20200101) |
Current International
Class: |
G06F
17/00 (20060101); G06F 17/27 (20060101) |
Field of
Search: |
;704/9 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Kawahara et al., "Identifying Contradictory and Contrastive
Relations between Statements to Outline Web Information on a Given
Topic", http://anthology.aclweb.org/C/C10/C10-2061.pdf, Coling
2010: Poster Volume, pp. 534-542, Beijing, Aug. 2010, 9 pages.
cited by applicant .
Nguyen et al., "Recognition of Agreement and Contradiction between
Sentences in Support-Sentence Retrieval",
http://da.xmachina.gr/Proceedings/KICSS2013/files/papers/paper15.pdf,
Proceedings of KICSS'2013, pp. 413-424, Progress & Business
Publishers, Krakow, 2013, 12 pages. cited by applicant .
Anonymous, "Method and System for Dynamically Assembling Video
Segments from Different Videos based on Search Criteria and a Video
Template", IP.com, Mar. 16, 2015, 4 pages. cited by applicant .
Mell et al., "The NIST Definition of Cloud Computing", NIST,
Special Publication 800-145, Sep. 2011, 7 pages. cited by
applicant.
|
Primary Examiner: Abebe; Daniel
Attorney, Agent or Firm: McLane; Christopher Wright; Andrew
D. Roberts Mlotkowski Safran Cole & Calderon, P.C.
Claims
What is claimed is:
1. A computer-implemented method comprising: receiving, by a
computing device, a plurality of content objects from one or more
computer content source devices; extracting, by the computing
device, metadata from the plurality of content objects; storing, by
the computing device, a plurality of records having the extracted
metadata in a repository, wherein each record identifies a time in
which a statement was made by an individual regarding a topic;
identifying, by the computing device, correlated content between
the plurality of content objects based on comparing the metadata in
the records, wherein the correlated content includes a plurality of
statements made by the individual regarding the topic at different
periods of time; generating, by the computing device, a correlated
content object having the correlated content; and providing, by the
computing device, the correlated content object to a user device,
wherein: the correlated content object includes a visual comparison
of a plurality of statements made by the individual regarding the
topic at different points in time, and the correlated content
object emphasizes a contradictory statement in the plurality of
statements.
2. The method of claim 1, wherein the plurality of content objects
are selected form a group consisting of: live broadcasted content;
audio content; video content; text content; and social media
content.
3. The method of claim 1, wherein the extracting the metadata
includes at least one selected from the group consisting of:
identifying the metadata from predefined metadata included in the
plurality of content objects; performing speech to text processing
on the plurality of content objects; performing natural language
processing on the plurality of content objects; and performing
image analysis on the plurality of content objects.
4. The method of claim 3, wherein the extracting the metadata
further includes identifying the individual and the statement made
by the individual regarding the topic.
5. The method of claim 4, wherein the extracting the metadata
further includes identifying a time index range in which the
statement was made by the individual regarding the topic.
6. The method of claim 4, wherein the extracting the metadata
further includes identifying a sentiment associated with the
statement made regarding the topic.
7. The method of claim 6, further comprising receiving criteria for
generating the correlated content object, wherein the identifying
the correlated content includes identifying the records with
metadata matching the criteria.
8. The method of claim 6, further comprising: receiving real-time
content; and extracting metadata from the real-time content,
wherein the identifying the correlated content includes identifying
the records with metadata matching the extracted metadata from the
real-time content.
9. The method of claim 1, wherein a service provider at least one
of creates, maintains, deploys and supports the computing
device.
10. The method of claim 1, wherein the receiving the plurality of
content objects, the extracting the metadata, the storing the
plurality of records, the identifying the correlated content, the
generating the correlated content object, and the providing the
correlated content object are provided by a service provider on a
subscription, advertising, and/or fee basis.
11. The method of claim 1, wherein the computing device includes a
storage device comprising software provided as a service in a cloud
environment.
12. The method of claim 1, further comprising deploying a system
for comparing statements made by the individual regarding the topic
at different points in time, comprising providing a computer
infrastructure operable to perform the receiving the plurality of
content objects, the extracting the metadata, the storing the
plurality of records, the identifying the correlated content, the
generating the correlated content object, and the providing the
correlated content object.
13. A computer program product for comparing statements made by an
individual regarding a topic at different points in time the
computer program product comprising a computer readable storage
medium having program instructions embodied therewith, the program
instructions executable by a computing device to cause the
computing device to: receive a plurality of content objects from
one or more computer content source devices; extract metadata from
the plurality of content objects; store a plurality of records
having the extracted metadata in a repository, wherein each record
identifies a time in which a statement was made by an individual
regarding a topic; receive real-time content; extract metadata from
the real-time content; identify correlated content based on
comparing the metadata in the records with the extracted metadata
from the real-time content, wherein the correlated content includes
a statement made by the individual regarding the topic at a prior
time; generate a correlated content object having the correlated
content; and provide the correlated content object to a user
device, wherein: the correlated content object includes a visual
comparison of a plurality of statements made by the individual
regarding the topic at different points in time, and the correlated
content object emphasizes a contradictory statement in the
plurality of statements.
14. The computer program product of claim 13, wherein the plurality
of content objects are selected form a group consisting of: live
broadcasted content; audio content; video content; text content;
and social media content.
15. The computer program product of claim 13, wherein the
extracting the metadata from the plurality of content objects and
from the real-time content includes at least one selected from the
group consisting of: identifying the metadata from predefined
metadata included in the plurality of content objects; performing
speech to text processing on the plurality of content objects;
performing natural language processing on the plurality of content
objects; and performing image analysis on the plurality of content
objects.
16. The computer program product of claim 15, wherein the
extracting the metadata from the plurality of content objects and
from the real-time content further includes identifying the
individual and the statement made by the individual regarding the
topic.
17. The computer program product of claim 16, wherein the
extracting the metadata from the plurality of content objects and
from the real-time content further includes identifying a sentiment
associated with the statement made regarding the topic.
18. A system comprising: a CPU, a computer readable memory and a
computer readable storage medium associated with a computing
device; program instructions to receive a plurality of content
objects from one or more computer content source devices; program
instructions to extract metadata from the plurality of content
objects; program instructions to store a plurality of records
having the extracted metadata in a repository, wherein each record
identifies a time in which a statement was made by an individual
regarding a topic; program instructions to receive content
correlating criteria; program instructions to identify correlated
content based on comparing the metadata in the records with the
content correlating criteria, wherein the correlated content
includes a plurality of statements made by the individual regarding
the topic at different points in time; program instructions to
generate a correlated content object having the correlated content;
and program instructions to provide the correlated content object
to a user device, wherein: the correlated content object includes a
visual comparison of a plurality of statements made by the
individual regarding the topic at different points in time, and the
correlated content object emphasizes a contradictory statement in
the plurality of statements wherein the program instructions are
stored on the computer readable storage medium for execution by the
CPU via the computer readable memory.
19. The system of claim 18, wherein the extracting the metadata
includes at least one selected from the group consisting of:
identifying the metadata from predefined metadata included in the
plurality of content objects; performing speech to text processing
on the plurality of content objects; performing natural language
processing on the plurality of content objects; and performing
image analysis on the plurality of content objects.
Description
BACKGROUND
The present invention generally relates to identifying correlated
content associated with an individual and, more particularly, to
identifying correlated content associated with an individual for
comparing statements made by the individual at different points in
time.
Statements made by public figures (e.g., athletes, coaches,
business leaders, etc.) are often broadcast, stored, and/or printed
via multiple different content sources (e.g., live television,
internet television, electronic video/audio repositories,
newspapers, magazines, etc.). Such statements are often heavily
scrutinized by the public to determine the public figure's
standpoint or view on various topics. Statements made by an
individual (e.g., a public figure) regarding a position, view, or
standpoint on a topic may change or remain consistent over
time.
SUMMARY
In an aspect of the invention, a computer-implemented method
includes: receiving, by a computing device, a plurality of content
objects from one or more computer content source devices;
extracting, by the computing device, metadata from the plurality of
content objects; storing, by the computing device, a plurality of
records having the extracted metadata in a repository, wherein each
record identifies a time in which a statement was made by an
individual regarding a topic; identifying, by the computing device,
correlated content between the plurality of content objects based
on comparing the metadata in the records, wherein the correlated
content includes a plurality of statements made by the individual
regarding the topic at different periods of time; generating, by
the computing device, a correlated content object having the
correlated content; and providing, by the computing device, the
correlated content object to a user device.
In an aspect of the invention, there is a computer program product
for comparing statements made by an individual regarding a topic at
different points in time the computer program product comprising a
computer readable storage medium having program instructions
embodied therewith. The program instructions are executable by a
computing device to cause the computing device to: receive a
plurality of content objects from one or more computer content
source devices; extract metadata from the plurality of content
objects; store a plurality of records having the extracted metadata
in a repository, wherein each record identifies a time in which a
statement was made by an individual regarding a topic; receive
real-time content; extract metadata from the real-time content;
identify correlated content based on comparing the metadata in the
records with the extracted metadata from the real-time content,
wherein the correlated content includes a statement made by the
individual regarding the topic at a prior time; generate a
correlated content object having the correlated content; and
provide the correlated content object to a user device.
In an aspect of the invention, a system includes: a CPU, a computer
readable memory and a computer readable storage medium associated
with a computing device; program instructions to receive a
plurality of content objects from one or more computer content
source devices; program instructions to extract metadata from the
plurality of content objects; program instructions to store a
plurality of records having the extracted metadata in a repository,
wherein each record identifies a time in which a statement was made
by an individual regarding a topic; program instructions to receive
content correlating criteria; program instructions to identify
correlated content based on comparing the metadata in the records
with the content correlating criteria, wherein the correlated
content includes a plurality of statements made by the individual
regarding the topic at different points in time; program
instructions to generate a correlated content object having the
correlated content; and program instructions to provide the
correlated content object to a user device. The program
instructions are stored on the computer readable storage medium for
execution by the CPU via the computer readable memory.
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention is described in the detailed description
which follows, in reference to the noted plurality of drawings by
way of non-limiting examples of exemplary embodiments of the
present invention.
FIG. 1 depicts a cloud computing node according to an embodiment of
the present invention.
FIG. 2 depicts a cloud computing environment according to an
embodiment of the present invention.
FIG. 3 depicts abstraction model layers according to an embodiment
of the present invention.
FIG. 4 shows an overview of an example implementation in accordance
with aspects of the present invention
FIG. 5 shows an example environment in accordance with aspects of
the present invention.
FIG. 6 shows a block diagram of example components of a content
correlation server in accordance with aspects of the present
invention.
FIG. 7 shows an example data structure of extracted metadata for
content objects in accordance with aspects of the present
invention.
FIG. 8 shows an example flowchart of a process for storing
extracted metadata for building a data structure that stores
statements made by individuals over a period of time in accordance
with aspects of the present invention.
FIG. 9 shows an example process for extracting metadata for content
objects in accordance with aspects of the present invention.
FIG. 10 shows an example process for generating a correlated
content object in real-time in accordance with aspects of the
present invention.
FIG. 11 shows an example process for generating a correlated
content object based on receiving criteria from a user in
accordance with aspects of the present invention.
FIG. 12 shows an example implementation for presenting a correlated
content object in accordance with aspects of the present
invention.
DETAILED DESCRIPTION
The present invention generally relates to identifying correlated
content associated with an individual and, more particularly, to
identifying correlated content associated with an individual for
comparing statements made by the individual at different points in
time. Evaluating an individual's credibility and/or position on a
topic may require evaluating the individual's statements on the
topic over a period of time to determine whether the individual's
position changed over time, and to what degree. Accordingly,
aspects of the present invention may collect content from various
sources over a period of time in which the collected content
includes an individual's statements regarding a topic over a period
of time. Further, aspects of the present invention may present
correlated content regarding statements made by the individual
regarding a particular topic in the form of a collage or other form
in which the statements made by the individual over a period of
time can be easily compared. In embodiments, aspects of the present
invention may generate a credibility or consistency score regarding
an individual's consistency of statements regarding a particular
topic.
As described in greater detail herein, aspects of the present
invention may extract metadata from content objects obtained from
various content sources, and may store the metadata in a structured
manner. For example, aspects of the present invention may extract
metadata from a content object (e.g., a video) that identifies an
individual that is speaking in the video, a date/time, a
transcription of what was spoken, a topic, the individual's
position/sentiment regarding the topic, and a link to the content
object. Over a period of time, additional content objects are
obtained from various content sources and metadata is extracted
from the content objects and stored in a repository. Using the
metadata in the repository, aspects of the present invention may
identify statements made by an individual regarding a topic over a
period of time. Further, aspects of the present invention may
generate a correlated content object that may include, for example,
a collage or other type of comparison of statements made by the
individual regarding the particular topic over a period of time. In
this way, the individual's statements may be compared to determine
whether the individual's position/sentiment regarding the topic
changed over time, and to what degree.
As described herein, aspects of the present invention may collect
publically available video, audio, text, and/or other types of
content from multiple content sources (e.g., television broadcast
feeds, internet videos, printed/electronically published articles,
social media posts, etc.). For certain individuals (e.g., public
figures), the sheer volume of content that is published may be from
hundreds to thousands of videos, articles, etc. per year. Further,
the number of topic discussed and the number of statements made by
an individual that are published may be vast. Thus, it is urged
that processes of embodiments of the present invention (e.g., the
collecting, and extracting of metadata regarding statements made by
an individual on different topics) require the use of computing
technology and cannot be done simply with pen and paper. Further,
aspects of the present invention improve the functioning of
computer technology itself by improving the efficiency in which
data is collected, processed, and presented. As such, aspects of
the present invention curb and reduce computer resource consumption
and thus, improve the functioning of computer technology
itself.
The present invention may be a system, a method, and/or a computer
program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
nonremovable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and content
correlation 96.
Referring back to FIG. 1, the program/utility 40 may include one or
more program modules 42 that generally carry out the functions
and/or methodologies of embodiments of the invention as described
herein (e.g., such as the functionality provided by content
correlation 96). Specifically, the program modules 42 may receive
content objects across multiple content sources, extract metadata
from the content objects, and store the extracted metadata in
individual records with each record identifying an individual, a
topic, statements made about the topic, sentiment/position on the
topic, date/time in which the statement was made, and/or a link to
the content object. The program modules 42 may also compare stored
extracted metadata with metadata associated with live content
and/or associated with user-defined criteria, create a correlated
content object having content objects that are correlated with the
live content and/or with user-defined criteria (e.g., content
objects having statements regarding a topic made by the
individual), and output the correlated content object to display
comparison between statements made by the individual regarding a
topic over a period of time. Other functionalities of the program
modules 42 are described further herein such that the program
modules 42 are not limited to the functions described above.
Moreover, it is noted that some of the modules 42 can be
implemented within the infrastructure shown in FIGS. 1-3. For
example, the modules 42 may be representative of a content
correlation server as shown in FIG. 4.
FIG. 4 shows an overview of an example implementation in accordance
with aspects of the present invention. As shown in FIG. 4, a
content correlation server 220 may receive content objects
associated with an individual from multiple content source devices
230 (step 1.1). For example, the content correlation server 220 may
receive content objects, such as video (e.g., from live-broadcasted
television, pre-recorded videos, etc.), text from published
articles, content from social media platforms, etc. As described
herein, the content objects may relate to statements made by
individuals regarding various topics.
At step 1.2, the content correlation server 220 may extract
metadata associated with the content objects in a structured
format. For example, the content correlation server 220 may extract
metadata associated with the content object, such as an individual
that was speaking, a transcript of what was spoken by the
individual, a topic, the individual's position/sentiment on the
topic, a link to where the content object may be accessed or
obtained, etc.
As described herein, metadata may be included as part of the
content object. For example, certain content objects (e.g., video
files, text files, etc.) may include a header that contains
metadata, or may be accompanied by a file that contains metadata.
Additionally, or alternatively, metadata may be obtained via
image/speech recognition techniques (e.g., to identify an
individual whose speech, image, and/or video is included in the
content object). Additionally, or alternatively, the metadata may
be obtained via speech to text techniques (e.g., to create a
transcription of words spoken by the individual). Additionally, or
alternatively, the metadata may be obtained via natural language
processing techniques (e.g., to determine a topic and/or
individual's position regarding the topic).
In embodiments, the content correlation server 220 may store the
metadata in a structured format in which the metadata is stored in
individual records. As described herein, each record may identify
the date/time of the content object, an individual whose speech,
image, and/or video is included in the content object, a
transcription of statements made by the individual, a topic
associated with the statements, the individual's position and/or
sentiment regarding the topic, a link where the object may be
accessed, and/or a time-index relating to a portion of the content
object (e.g., a portion of a video or audio track) in which the
statements were made. In embodiments, separate records may be
generated for the statements made by the same individual regarding
different topics. For example, for a content object (e.g., a video
of a press-conference), different metadata records may be stored
for different topics discussed by the individual (e.g., to more
easily compare statements made by the individual regarding specific
topics).
At step 1.3, the content correlation server 220 may generate a
correlated content object. For example, the content correlation
server 220 may generate a correlated content object that may
include a collage and/or other representation of statements made by
an individual regarding a topic over a period of time. In
embodiments, the a correlated content object may be generated in
real-time in which real-time or live statements being made by the
individual regarding a topic may be compared to prior statements
made by the individual regarding the topic. As described herein,
real-time or live content may be analyzed to identify the
individual, identify statements being made by the individual,
identify the topic being discussed, and look up, from stored
metadata records prior records and content objects having instances
in which the same individual previously made statements about the
same topic. As an illustrative non-limiting example, the content
correlation server 220 may identify that an individual (e.g., a
coach of a sports team) is currently discussing the construction of
a new stadium. The content correlation server 220 may identify,
from stored metadata records, content objects in which the
individual previously made statements about the construction of a
new stadium. The content correlation server 220 may generate a
correlated content object having those content objects in which the
individual previously made statements about the construction of a
new stadium (e.g., in the form of a video/still image collage, a
comparison table, etc.).
In embodiments, the content correlation server 220 may generate a
correlated content object based on user-defined criteria. For
example, a user may define which individual, topic, and date range
with which to create a correlated content object that identifies
content objects having statements made by the individual regarding
the topic over a period of time. In embodiments, correlated content
object may identify a consistency between current statements and
prior statements made by the individual (e.g., by comparing a
current position and a prior position on the topic).
At step 1.4, the content correlation server 220 may provide the
correlated content object to the user device 210. In embodiments,
the user device 210 may display the correlated content object. In
one example, the user device 210 may overlay the correlated content
object over a live broadcast to allow the user to quickly identify
similarities and/or discrepancies between prior statements and
current statements made by the individual. Additionally, or
alternatively, the content object may be played back by the user at
a later time and may be stored in the form of a video file and/or
other type of computer file.
FIG. 5 shows an example environment in accordance with aspects of
the present invention. As shown in FIG, 5, environment 500 may
include a user device 210, a content correlation server 220,
content source devices 230, and network 240. In embodiments, one or
more components in environment 500 may correspond to one or more
components in the cloud computing environment of FIG. 2. In
embodiments, one or more components in environment 500 may include
the components of computer system/server 12 of FIG. 1.
The user device 210 may include a device capable of communicating
via a network, such as the network 240. For example, the user
device 210 may correspond to a mobile communication device (e.g., a
smart phone or a personal digital assistant (PDA)), a portable
computer device (e.g., a laptop or a tablet computer), or another
type of device. In some embodiments, the user device 210 may be
used to provide criteria for generating a correlated content
object. For example, the user device 210 may provide criteria for
generating a correlated content object including content objects
having statements made by a particular individual regarding a
particular topic over a period of time. The user device 210 may
receive and display content objects in order for a user to easily
compare statements made by the individual over a period of
time.
The content correlation server 220 may include one or more
computing devices (e.g., such as computer system/server 12 of FIG.
1) that receives content objects from the content source devices
230, extracts metadata from the content objects, stores the
metadata in individual records, identifies correlated content
objects based on the stored metadata, and generates a correlated
content object. The content correlation server 220 may generate a
correlated content object based on criteria received from the user
device 210 and/or based on the metadata of live-broadcasted
content.
The content source devices 230 may include one or more computing
devices (e.g., such as computer system/server 12 of FIG. 1) that
stores and provides content objects to the content correlation
server 220. For example, the content source devices 230 may include
sources associated with social media platform, news platforms,
television broadcasting systems, or the like. The content source
devices 230 may store electronic content objects consumable only by
computing devices, such as the content correlation server 220, user
device 210, and/or other electronic computing devices.
The network 240 may include network nodes, such as network nodes 10
of FIG. 2. Additionally, or alternatively, the network 240 may
include one or more wired and/or wireless networks. For example,
the network 240 may include a cellular network (e.g., a second
generation (2G) network, a third generation (3G) network, a fourth
generation (4G) network, a fifth generation (5G) network, a
long-term evolution (LTE) network, a global system for mobile (GSM)
network, a code division multiple access (CDMA) network, an
evolution-data optimized (EVDO) network, or the like), a public
land mobile network (PLMN), and/or another network. Additionally,
or alternatively, the network 240 may include a local area network
(LAN), a wide area network (WAN), a metropolitan network (MAN), the
Public Switched Telephone Network (PSTN), an ad hoc network, a
managed Internet Protocol (IP) network, a virtual private network
(VPN), an intranet, the Internet, a fiber optic-based network,
and/or a combination of these or other types of networks.
The quantity of devices and/or networks in the environment 500 is
not limited to what is shown in FIG. 5. In practice, the
environment 500 may include additional devices and/or networks;
fewer devices and/or networks; different devices and/or networks;
or differently arranged devices and/or networks than illustrated in
FIG. 5. Also, in some implementations, one or more of the devices
of the environment 500 may perform one or more functions described
as being performed by another one or more of the devices of the
environment 500. Devices of the environment 500 may interconnect
via wired connections, wireless connections, or a combination of
wired and wireless connections.
FIG. 6 shows a block diagram of example components of a content
correlation server in accordance with aspects of the present
invention. As shown in FIG. 6, the content correlation server 220
may include a content receiving module 610, a content metadata
extraction module 620, a content metadata repository 630, and a
correlated content object generation module 640. In embodiments,
the content correlation server 220 may include additional or fewer
components than those shown in FIG. 6. In embodiments, separate
components may be integrated into a single computing component or
module. Additionally, or alternatively, a single component may be
implemented as multiple computing components or modules.
The content receiving module 610 may include a program module
(e.g., program module 42 of FIG. 1) that receives content from one
or more content source devices 230. For example, the content
receiving module 610 may periodically request content and/or
receive content as part of a content receiving service. In
embodiments, the content receiving module 610 may receive a variety
of content, including but not limited to live broadcast content,
pre-recorded content, video content, audio content, text content,
etc.
The content metadata extraction module 620 may include a program
module (e.g., program module 42 of FIG. 1) that extracts metadata
from the content received by the content receiving module 610. For
example, the content metadata extraction module 620 may extract
metadata, such as information identifying an individual that is
speaking in the video, a date/time, a transcription of what was
spoken, a topic, the individual's position/sentiment regarding the
topic, and a link to the content object. In embodiments, the
content metadata extraction module 620 may extract metadata when
certain content objects (e.g., video files, text files, etc.)
include predefined metadata (e.g., in a header of the content
object), or when the content object is accompanied by a file that
contains metadata. Additionally, or alternatively, the content
metadata extraction module 620 may obtain metadata via image/speech
recognition techniques (e.g., to identify an individual whose
speech, image, and/or video is included in the content object).
Additionally, or alternatively, the content metadata extraction
module 620 may obtain metadata via speech to text techniques (e.g.,
to create a transcription of words spoken by the individual).
Additionally, or alternatively, the content metadata extraction
module 620 may obtain metadata via natural language processing
techniques (e.g., to determine a topic and/or individual's position
regarding the topic).
The content metadata repository 630 may include a data storage
device (e.g., storage system 34 of FIG. 1) that stores the metadata
in a structured format. For example, the content metadata
repository 630 may store metadata within individual records in
which each record may identify the date/time of a content object,
an individual whose speech, image, and/or video is included in the
content object, a transcription of statements made by the
individual, a topic associated with the statements, the
individual's position and/or sentiment regarding the topic, a link
where the object may be accessed, and/or a time-index relating to a
portion of the content object (e.g., a portion of a video or audio
track) in which the statements were made. Over a period of time, as
additional content is received by the content receiving module 610
and as metadata for the content is extracted by the content
metadata extraction module 620, the content metadata repository 630
may continue to store additional records as described above. In
this way, a profile of an individual having the individual's
statements regarding various topics may continue to build and grow
for a more comprehensive analysis and comparison of the
individual's statements over a period of time.
The correlated content object generation module 640 may include a
program module (e.g., program module 42 of FIG. 1) that may
generate a correlated content object. For example, the correlated
content object generation module 640 may generate a correlated
content object that may include a collage of videos, images, text,
stitched video, comparison table of text, and/or other
representation of statements made by an individual regarding a
topic over a period of time. In embodiments, the correlated content
object may be generated in real-time in which real-time or live
statements being made by the individual regarding a topic may be
compared to prior statements made by the individual regarding the
topic. As described herein, real-time or live content may be
analyzed to identify the individual, identify statements being made
by the individual, identify the topic being discussed, and look up,
from stored metadata records prior records and content objects
having instances in which the same individual previously made
statements about the same topic.
In embodiments, the correlated content object generation module 640
may generate a correlated content based on user-defined criteria.
For example, a user may define which individual, topic, and date
range with which to create a correlated content object that
identifies content objects having statements made by the individual
regarding the topic over a period of time. In embodiments, the
correlated content object may identify a measure of consistency
between current statements and prior statements made by the
individual (e.g., by comparing a current position and a prior
position on the topic). For example, in a situation in which when
contradictory statements are made, the correlated content object
may include a message to emphasize the contradictory statement.
Alternatively, when consistent statements are made, the correlated
content object may include a message to emphasize the consistent
statement. In embodiments, the correlated content object generation
module 640 may provide the correlated content object for display on
a user device 210.
FIG. 7 shows an example data structure of extracted metadata for
content objects in accordance with aspects of the present
invention. As shown in FIG. 7, data structure 700 may store data
records having metadata for content objects. In the example shown,
each record may identify a speaker (e.g., name of an individual)
present in the content object, a date of the content object, a
topic discussed, a transcription of statements made by the
individual, a sentiment, and a location of the content object. As
described herein, the location of the content object may identify a
URL/link where the content object may be accessed. Also, the
location of the content object may identify a time index associated
with the speaker and the topic discussed.
As described herein, data structure 700 may store separate records
for the same content object when the same content object includes
different speakers and discussions of different topics. For
example, for a single video file, different speakers and/or topics
may be included in the video file. However, metadata for different
portions of the content file (e.g., time periods of the video) may
include different speakers and/or discussion of different topics.
Accordingly, the data structure 700 may store different records for
a single content object in which each record identifies the time
index associated with a particular speaker/individual discussing a
particular topic.
In the examples of FIG. 7, the data structure 700 may store two
records associated with a particular individual (e.g., Jon Smith)
regarding statements made about a particular topic (e.g., Stadium
Building). As shown in FIG. 7, the two records may identify
contradictory statements made by the individual at two different
points in time (e.g., based on contradictory sentiments). As
described herein, the information stored by the data structure 700
may be used to generate a correlated content object in which
content from a particular individual regarding a particular topic
may be presented in a collage, stitched video, and/or other
format.
FIG. 8 shows an example flowchart of a process for storing
extracted metadata for building a data structure that stores
statements made by individuals over a period of time in accordance
with aspects of the present invention. The steps of FIG. 8 may be
implemented in the environment of FIG. 5, for example, and are
described using reference numbers of elements depicted in FIG. 5.
As noted above, the flowchart illustrates the architecture,
functionality, and operation of possible implementations of
systems, methods, and computer program products according to
various embodiments of the present invention.
As shown in FIG. 8, process 800 may include receiving content
across content sources (step 810). For example, as described above
with respect to the, the content correlation server 220 may
periodically request content and/or receive content as part of a
content receiving service. In embodiments, the content correlation
server 220 may receive a variety of content, including live
broadcast content, pre-recorded content, video content, audio
content, text content, etc.
Process 800 may also include extracting metadata from the content
(step 820). For example, as described above with respect to the
content metadata extraction module 620, the content correlation
server 220 may extract metadata from the content received by the
content receiving module 610. For example, the content correlation
server 220 may extract metadata, such as information identifying an
individual that is speaking in the video, a date/time, a
transcription of what was spoken, a topic, the individual's
position/sentiment regarding the topic, and a link to the content
object. Additional details regarding the extraction of the metadata
are described below with respect to FIG. 9.
Process 800 may further include storing extract metadata for
content objects in a content metadata repository (step 830). For
example, as described above with respect to the content metadata
repository 630, the content correlation server 220 may store the
metadata in a structured format. For example, the content
correlation server 220 may store metadata within individual records
in which each record may identify the date/time of a content
object, an individual whose speech, image, and/or video is included
in the content object, a transcription of statements made by the
individual, a topic associated with the statements, the
individual's position and/or sentiment regarding the topic, a link
where the object may be accessed, and/or a time-index relating to a
portion of the content object (e.g., a portion of a video or audio
track) in which the statements were made.
FIG. 9 shows an example process for extracting metadata for content
objects in accordance with aspects of the present invention. The
steps of FIG. 9 may be implemented in the environment of FIG. 5,
for example, and are described using reference numbers of elements
depicted in FIG. 5. As noted above, the flowchart illustrates the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In
embodiments, process 900 of FIG. 9 may correspond to process step
820 in process 800 of FIG. 8.
As shown in FIG. 9, process 900 may include identifying predefined
metadata in a content object (step 910). For example, as described
above with respect to the content metadata extraction module 620,
the content correlation server 220 may identify predefined metadata
included in the content object included in a header of the content
object or in a file accompanied by the content object. As described
herein, the predefined metadata may include any or all of the
metadata that is stored by the content metadata repository 630 of
the content correlation server 220. For example, the predefined
metadata may include one or more of: information identifying an
individual that is speaking in the video, a date/time, a
transcription of what was spoken, a topic, the individual's
position/sentiment regarding the topic, and a link to the content
object.
In practice, however, predefined metadata may not exist or may not
identify all of the metadata that is to be stored. Accordingly,
process 900 may include additional process steps for extracting
metadata that may not be predefined. For example, process 900 may
further include performing speech to text processing (step 920). In
embodiments, the content correlation server 220 may perform speech
to text processing to convert audio from the content object into a
transcription of statements.
Process 900 may also include performing natural language processing
(step 930). For example, as described above with respect to the
content metadata extraction module 620, the content correlation
server 220 may perform natural language processing to identify a
topic discussed from the text transcription. If the content object
is a text or article, the content correlation server 220 may
perform natural language processing on the text included in the
content object.
Process 900 may further include performing image analysis (step
940). For example, as described above with respect to the content
metadata extraction module 620, the content correlation server 220
may perform image analysis and/or facial recognition to identify an
individual included in the content object.
Process 900 may also include identifying a topic and sentiment from
the predefined metadata, speech to text processing, and/or natural
language processing (step 950). For example, the content
correlation server 220 may identify the topic and sentiment from
the predefined metadata. If the topic is not identified in the
predefined metadata, the content correlation server 220 may
identify the topic from the speech to text processing and/or the
natural language processing.
Process 900 may further include identifying a speaker from the
predefined metadata and/or the image analysis (step 960). For
example, the content correlation server 220 may identify the
speaker from the predefined metadata. Additionally, or
alternatively, the content correlation server 220 may identify the
speaker from the image analysis if the speaker is not identified
from the predefined metadata.
Process 900 may also include identifying a distinct time index
range for each speaker and topic discussed (step 970). For example,
based on performing natural language processing, the content
correlation server 220 may identify portions of the transcription
in which a particular topic was discussed. The content correlation
server 220 may also identify a time index range corresponding to
the topic (e.g., a time index range for a video). In embodiments,
the content correlation server 220 may store the metadata, as
obtained by using process 900, in individual records as described
herein.
FIG. 10 shows an example process for generating a correlated
content object in real-time in accordance with aspects of the
present invention. The steps of FIG. 10 may be implemented in the
environment of FIG. 5, for example, and are described using
reference numbers of elements depicted in FIG. 5. As noted above,
the flowchart illustrates the architecture, functionality, and
operation of possible implementations of systems, methods, and
computer program products according to various embodiments of the
present invention.
As shown in FIG. 10, process 1000 may include receiving real-time
content from a single source (step 1010). For example, the content
correlation server 220 may receive real-time content from a single
source, such as a single video file, broadcast, etc. As described
herein, the content correlation server 220 may generate a
correlated content object based on statements made by the
individual in real-time as presented in the single source.
Process 1000 may further include extracting metadata for the
real-time content (step 1020). For example, the content correlation
server 220 may extract metadata for the real-time content in a
similar manner as is discussed with respect to the content metadata
extraction module 620 and process 900.
Process 1000 may also include comparing the extracted metadata for
the real-time content with stored content object metadata (step
1030). For example, the content correlation server 220 may compare
the extracted metadata for the real-time content (e.g., a speaker
and a topic of discussion) with stored content object metadata
(e.g., records stored by the content metadata repository 630).
Process 1000 may further include identifying correlated content
(step 1040). For example, the content correlation server 220 may
identify correlated content based on the comparing. In particular,
the content correlation server 220 may identify correlated content
that match the same speaker and topic as that of the real-time
content. As a specific, non-limiting example, assume that the
real-time content includes the speaker Jon Smith discussing the
topic of stadium building (e.g., as determined based on extracting
the metadata for the real-time content at process step 1020).
Accordingly, the content correlation server 220 may identify
correlated content having metadata records identifying the same
speaker and the same topic. The content correlation server 220 may
also identify the location of the correlated content.
Process 1000 may include creating a correlated content object (step
1050). For example, the content correlation server 220 may create a
correlated content object by obtaining the correlated content from
the location identified in the records of the correlated content.
Further, the content correlation server 220 may create the
correlated content in the form of a collage of videos, images,
text, stitched video, comparison table of text, and/or other
representation of statements made by an individual regarding a
topic over a period of time. As described herein, the correlated
content object may include a visual indicator to highlight or
emphasize consistencies or inconsistencies in the statements.
Process 1000 may also include outputting the correlated content
object (step 1060). For example, the content correlation server 220
may output the correlated content object to a user device 210
(e.g., a user device 210 that is registered and/or has requested to
receive correlated content objects for real-time content).
FIG. 11 shows an example process for generating a correlated
content object based on receiving criteria from a user in
accordance with aspects of the present invention. The steps of FIG.
11 may be implemented in the environment of FIG. 5, for example,
and are described using reference numbers of elements depicted in
FIG. 5. As noted above, the flowchart illustrates the architecture,
functionality, and operation of possible implementations of
systems, methods, and computer program products according to
various embodiments of the present invention.
Process 1100 may include receiving correlated content object
criteria (step 1110). For example, the content correlation server
220 may receive correlated content object criteria from a user
device 210. In embodiments, the correlated content object criteria
may identify a speaker, a time period, and/or a topic. A user may
select the correlated content object criteria for comparing
statements made by the speaker regarding a topic over a period of
time. In embodiments, the correlated content object criteria may
also identify whether to identify only consistent statements,
inconsistent statements, or all statements.
Process 1100 may further include identifying content objects
matching the criteria by looking up the criteria in a stored
metadata repository (step 1120). For example, the content
correlation server 220 may look up the criteria in the content
metadata repository 630 to identify records that match the criteria
(e.g., records associated with the speaker, topic, and date range
stipulated by the criteria).
Process 1100 may further include creating a correlated content
object (step 1130). For example, the content correlation server 220
may create the correlated content object based on the identified
content objects matching the criteria. In embodiments, the content
correlation server 220 may create the correlated content object as
described above with respect to the correlated content object
generation module 640 and process step 1050. Process 1100 may also
include outputting the correlated content object (step 1140). For
example, the content correlation server 220 may output the
correlated content object (e.g., in a similar manner as described
above with respect to the correlated content object generation
module 640 and process step 1060).
FIG. 12 shows an example implementation for presenting a correlated
content object as described herein. As shown in FIG. 12, the
content correlation server 220 may provide a correlated content
object to a user device 210. In the example of FIG. 12, the
correlated content object may include a video embedded next to live
content. For example, as shown in interface 1200, the live content
may identify a speaker making a statement that favors the building
of a stadium. In accordance with the processes described herein,
the content correlation server 220 may extract metadata for the
live content to identify a speaker and topic, lookup the same
speaker and topic in a metadata repository, identify correlated
content associated with the same speaker and topic (e.g., a video
of the speaker previously discussing the same topic), and present
the correlated content as an object that is embedded side-by-side
with live content. Also, the correlated content may include a
narrative describing what was previously stated by the individual.
In this way, a comparison may be made of current statements and
prior statements made by an individual regarding a particular
topic.
In embodiments, a service provider could offer to perform the
processes described herein. In this case, the service provider can
create, maintain, deploy, support, etc., the computer
infrastructure that performs the process steps of the invention for
one or more customers. These customers may be, for example, any
business that uses technology. In return, the service provider can
receive payment from the customer(s) under a subscription and/or
fee agreement and/or the service provider can receive payment from
the sale of advertising content to one or more third parties.
In still additional embodiments, the invention provides a
computer-implemented method, via a network. In this case, a
computer infrastructure, such as computer system/server 12 (FIG.
1), can be provided and one or more systems for performing the
processes of the invention can be obtained (e.g., created,
purchased, used, modified, etc.) and deployed to the computer
infrastructure. To this extent, the deployment of a system can
comprise one or more of: (1) installing program code on a computing
device, such as computer system/server 12 (as shown in FIG. 1),
from a computer-readable medium; (2) adding one or more computing
devices to the computer infrastructure; and (3) incorporating
and/or modifying one or more existing systems of the computer
infrastructure to enable the computer infrastructure to perform the
processes of the invention.
The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *
References